A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2004; you can also visit the original URL.
The file type is
This paper evaluates the application of the Minimum Classification Error (MCE) training to online-handwritten text recognition based on Hidden Markov Models. We describe an allograph-based, character level MCE training aimed at minimizing the character error rate while enabling flexibility in writing style. Experiments on a writer-independent discrete character recognition task covering all alpha-numerical characters and keyboard symbols show that MCE achieves more than 30% character error ratedoi:10.1109/icassp.2001.941223 dblp:conf/icassp/Biem01 fatcat:uh7lszwr2febjpfiq3myzb3j3q